Time Series Analysis > Exponential Smoothing Show
Contents: Exponential smoothing of time series data assigns exponentially decreasing weights for newest to oldest observations. In other words, the older the data, the less priority (“weight”) the data is given; newer data is seen as more relevant and is assigned more weight. Smoothing parameters (smoothing constants)— usually denoted by α— determine the weights for observations. Exponential smoothing is usually used to make short term forecasts, as longer term forecasts using this technique can be quite unreliable.
1. Simple Exponential SmoothingThe basic formula is:
Many alternative formulas exist. For example, Roberts (1959) replaced yt-1 with the current observation, yt. Another formula uses the forecast for the previous period and current period: Where:
Which formula to use is usually a moot point, as most exponential smoothing is performed using software. Whichever formula you use though, you’ll have to set an initial observation. This is a judgment call. You could use an average of the first few observations, or you could set the second smoothed value equal to the original observation value to get the ball rolling. 2. Double Exponential SmoothingThis method is deemed more reliable for analyzing data that shows a trend. In addition, this is a more complicated method which adds a second equation to the procedure:
Where:
3. Triple Exponential SmoothingIf your data shows a trend and seasonality, use triple exponential smoothing. In addition to the equations for single and double smoothing, a third equation is used to handle the seasonality aspect:
Like α and γ, the optimal Β minimizes the MSE. Exponential Smoothing in Excel 2016 & 2013Watch the video or read the steps below:
How to perform exponential smoothing in Excel 2013 Exponential Smoothing in Excel 2016 & 2013: OverviewExponential smoothing is a way to smooth out data for presentations or to make forecasts. It’s usually used for finance and economics. If you have a time series with a clear pattern, you could use moving averages — but if you don’t have a clear pattern you can use exponential smoothing to forecast.Damping FactorsPerhaps one of the most confusing aspects of exponential smoothing is the damping factor. Damping factors are used to smooth out the graph and take on a value between 0 and 1. Technically, the damping factor is 1 minus the alpha level (1 – α). But all you really need to know is smaller alpha levels (i.e. larger damping factors), smooths out the peaks and valleys more than larger alpha levels (smaller damping factors). Smaller damping factors also mean that your smoothed values are closer to the actual data points than larger damping factors. The easiest way to create exponential smoothing in Excel is to use the Data Analysis Toolpak. Exponential Smoothing in Excel 2016 & 2013: StepsStep 1: Click the “Data” tab and then click “Data Analysis.” Step 2: Select “Exponential Smoothing” and then click “OK.” Step 3: Click the Input Range box and then type the location for your forecast data. For example, if you typed your data into cells E1 to E10, type “E1:E10” into that box Step 4: Type a damping factor into the damping factor box. A valid value is 0 to 1. Don’t worry if you aren’t sure what damping factor to type in>you can easily repeat the tests with different damping factors (i.e. 0.9, 0.5, 0.3) to see which one works best. Step 5: Type a cell location into the Output range box. You generally want this in the next column. For example, if you typed your data into cells E1 to E10, type “F1” into that box Step 6: Click “OK.” The following graph shows the original data set (first column of data), and what happens when a damping factor is applied: Check out our YouTube channel for more Excel help and tips! ReferencesAgresti A. (1990) Categorical Data Analysis. John Wiley and Sons, New York. ---------------------------------------------------------------------------
Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. Your first 30 minutes with a Chegg tutor is free! Comments? Need to post a correction? Please Contact Us. When using simple exponential smoothing the value of the smoothing constant α can be?1. Simple Exponential Smoothing. Where: α = the smoothing constant, a value from 0 to 1.
Can alpha be greater than 1 in exponential smoothing?ALPHA is the smoothing parameter that defines the weighting and should be greater than 0 and less than 1.
What alpha value should I use for exponential smoothing?In practice, ALPHA is usually set to a value between 0.1 and 0.3.
Which of the following Cannot be the value for exponential smoothing constant?Answer and Explanation: The answer is A. between 0 and 1.
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